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14 - 18 April 2019
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Photonics research for agriculture, food safety & water quality

See what agricultural applications you can find at SPIE Defense + Commercial Sensing

Explore agricultural applications of sensing, imaging, and photonics technologies at SPIE Defense + Commercial Sensing, including UAVs, hyperspectral imaging, phenotyping, infrared thermography, and more.

Our topical tracks help you quickly locate potential items of interest in the 2018 Defense + Commercial Sensing program, such as sessions, papers, vendors, and courses. Explore the information below to see what may interest you. 

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23 March 2018

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30 March 2018

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Review the program

Below are conferences and papers that include significant technical content related to agricultural applications. SPIE Defense + Commercial Sensing 2018 includes 50 conferences and 1,900 papers and many of them may be of interest to those interested in agricultural applications, however these 6 conferences and 30 papers have been identified as containing specific content that may be of particular interest. 

Conferences

 • Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
 • Sensing for Agriculture and Food Quality and Safety
 • Next-Generation Spectroscopic Technologies
 • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
 • Thermosense: Thermal Infrared Applications
 • Energy Harvesting and Storage: Materials, Devices, and Applications

Papers

The 30 papers below are listed by start date and time.


Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management
Paper 10664-1

Author(s):  Mac, Utah State Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs
Date and Time: Monday, April 16, 2018, 8:15 AM

Small, unmanned aerial systems (sUAS) are used as remote sensing devices for agriculture with growing frequency. These systems place limitations on the types and quality of the cameras that can be flown. This, in turn, limits the quality of the information that can be generated for the grower. This paper statistically examines issues of how errors in sensor spectral response, orthorectification accuracy, and spatial resolution can affect the estimation of information products of potential interest to growers, such as plant nutrition and precision fertilization. The paper relies on high-resolution data collected in 2016 over a commercial vineyard located near Lodi, California.


Quality assessment of radiometric calibration of UAV image mosaics
Paper 10664-3

Author(s):  Cody, Texas A&M Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs
Date and Time: Monday, April 16, 2018, 9:05 AM

UAV (unmanned aerial vehicle) based imaging produces vast amounts of data that could be used to improve the efficiency of agricultural inputs. One reason this ability has not yet been realized is that producing radiometrically calibrated UAV image mosaics is difficult. This paper presents an investigation of a field-based image-mosaic calibration procedure. A commercial off-the-shelf fixed-wing small UAV and a five-band multispectral sensor were used with multiple exposure settings. We evaluate the quality of the radiometric calibration procedure for UAV image mosaics by comparing them to high quality calibrated manned aircraft and satellite images collected on the same day at roughly the same time.


LED spectral imaging with food and agricultural applications
Paper 10656-3

Author(s):  Jens Michael, DTU Compute, Technical Univ. of Denmark (Denmark), et al.
Conference10656: Image Sensing Technologies: Materials, Devices, Systems, and Applications V
Session 1: Advanced Hyperspectral Imaging I
Date and Time: Monday, April 16, 2018, 9:10 AM

Strobed LED spectral imaging systems share some principles with illumination filter wheel systems. The major advantages of strobed LED systems are: 1) speed, 2) no mechanical movement, 3) no dependency on unstable broad-spectrum incandescent light source, and 4) potential for high dynamic range imaging through the illumination. All of the above advantages are exploited in the proposed system where the spectral illumination source is combined with an integrating sphere and a calibration model that provides traceability, high reproducibility, spatial homogeneity, and focus on chemical properties of a heterogenous sample. Application areas of such systems are quite broad and high performance systems are seen within fields like agriculture, food, pharmaceuticals, medical devices, cosmetics, forensics, cultural heritage, and general manufacturing.


Correction of in-flight luminosity variations in multispectral UAS images, using a luminosity sensor and camera pair for improved biomass estimation in precision agriculture
Paper 10664-4

Author(s):  Jean-Marc, AgroParisTech (France), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs
Date and Time: Monday, April 16, 2018, 9:25 AM

This work studies the ability to correct luminosity variations on images from UAS flights under varying weather conditions. The Parrot SEQUOIA multispectral camera paired with its Sunshine luminosity sensor acquired data correlated with a field spectroradiometer on reference reflectance targets. Finally two different types of UAS carried out several sets of flights. All data were analyzed with and without the Sunshine sensor correction to quantify its improvement to the quality of reflectance measurements and biomass estimates.


Progress towards low resolution visible spectrometry with COTS components
Paper 10657-5

Author(s):  Alexander, SpectroClick, Inc. (United States), et al.
Conference10657: Next-Generation Spectroscopic Technologies XI
Session 1: Smartphone Spectroscopy
Date and Time: Monday, April 16, 2018, 10:00 AM

Portable spectrometers designed for users with little technical training must be more robust than spectrometers designed for professionals. Such instruments require software-driven operation using inexpensive components whose limitations are compensated algorithmically. We describe progress towards a hand-held grating spectrometer providing absorption, diffuse reflectance, and luminescence measurements. Low camera dynamic range is compensated by observing orders with a wide range of throughputs. This requires significant effort for wavelength calibration and inter-order intensity normalization. The paper discusses progress in automatic calibration algorithms, software modularization, and method development for quantifying nitrate and phosphate in agricultural runoff.


Detection of canola flowering using proximal and aerial remote sensing techniques
Paper 10664-8

Author(s):  Chongyuan, Washington State Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 2: Proximal and Remote Sensing for Phenotyping
Date and Time: Monday, April 16, 2018, 12:50 PM

In plant breeding, the time and length of flowering are important phenotypes that determine the seed yield potential in plants. Currently, flowering traits are visually assessed, which can be time-consuming, less accurate and subjective. To address this challenge, in this study, proximal and remote sensing with an unmanned aerial vehicle (UAV) were applied to monitor the canola flowers in a breeding trial with 35 varieties. Visible digital images (RGB) acquired were processed to extract the flowering features. The results indicated that flowering features extracted from both proximal and aerial images were significantly and positively correlated (P < 0.0001) with each other and with visual ratings. In general, aerial imaging overestimated canola flowering rates, which could be resulting from lower resolution at current altitude (30 m) and rendered lower correlation coefficients (r = 0.53 – 0.62) with visual ratings. Proximal sensing resulted in better estimation of canola flowering with r ranging from 0.65 to 0.91 and smaller intercepts, e.g. from 0.4% to 3.0% for percentage of flower, for linear relationship. This study indicated that remote sensing can be used for high-throughput phenotyping of canola flowers with confidence. High-throughput phenotyping techniques will potentially improve the throughput and objectivity of detecting flowers in canola and other crops, and contribute to the development of new cultivars in breeding programs and yield estimation in precision agriculture.


Phenotyping of sorghum panicles using unmanned aerial system (UAS) data
Paper 10664-9

Author(s):  Anjin, Texas A&M Univ. Corpus Christi (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 2: Proximal and Remote Sensing for Phenotyping
Date and Time: Monday, April 16, 2018, 1:40 PM

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days for precision agriculture and High Throughput Phenotyping (HTP). Attributes of sorghum panicle, especially, are critical information to assess overall crop condition, irrigation, and yield estimation. In this study, it is proposed a method to extract phenotypes of sorghum panicles using UAS data. UAS data were acquired with 85% overlap at an altitude of 10m above ground to generate super high resolution data. Orthomosaic, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the imagery from UAS. Sorghum panicles were identified from orthomosaic and DSM by using color ratio and circle fitting. The cylinder fitting method and disk tacking method were proposed to estimate panicle volume. Yield prediction models were generated between field-measured yield data and UAS-measured attributes of sorghum panicles.


Towards Development of Enhanced Pedestrian Safety Awareness at Crosswalks via Networked LiDAR, Thermal Imaging and Augmented Reality
Paper 10643-13

Author(s):  Zachary A. , Florida Polytechnic Univ. (United States), et al.
Conference10643: Autonomous Systems: Sensors, Vehicles, Security and the Internet of Everything
Session 2: Object Sensing for Detection, Classification, and Autonomous Operations
Date and Time: Monday, April 16, 2018, 2:40 PM

The system makes use of thermal imaging, LiDAR, conventional imagers, and sensors to distinguish between cars, people, animals, and other objects that may interact with a crosswalk at or near an intersection. A mesh network of these systems as nodes enables the coordination of information, alerts and/or interfaces to coordinate control of the lights as well as alert vehicles and people crossing at a crosswalk or an intersection. The data could also be used to enhance coordination of IoT or mobile devices such as those integrated with autonomous vehicles and the Intelligent Transportation System infrastructure to predict how to handle a pedestrian interaction with crosswalks or intersection. The goal of the system is to enhance pedestrian safety at crosswalks or intersections via LiDAR, thermal imaging, conventional imagers, shared interfaces, networks and other resources.


Inter-comparison of thermal measurements using ground-based sensors, airborne thermal cameras, and eddy covariance radiometers
Paper 10664-12

Author(s):  Alfonso F. , Utah State Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 3: Thermal and Hyperspectral Imaging from UAVs
Date and Time: Monday, April 16, 2018, 2:40 PM

The increasing availability of on-ground sensors and UAV-borne thermal cameras, along with eddy covariance radiometers, for estimation of agricultural parameters such as Evapotranspiration, implicitly rely on the assumption that information produced by these sensors is interchangeable or compatible. This work presents a comparison between on-ground infrared radiometer (IRT), microbolometer thermal cameras used in UAVs and thermal radiometers used in eddy covariance towers. as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program).


A low-cost method for collecting hyperspectral measurements from a small unmanned aircraft system
Paper 10664-15

Author(s):  Ali, Univ. of Kentucky (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 3: Thermal and Hyperspectral Imaging from UAVs
Date and Time: Monday, April 16, 2018, 4:10 PM

This study aimed to develop a spectral measurement platform for deployment on a sUAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using an embedded computer programmed to interface with the sUAS autopilot for autonomous data acquisition. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system.


Hyperspectral data analysis of the world's leading agricultural crops
Paper 10639-40

Author(s):  Prasad S. , U.S. Geological Survey (United States), et al.
Conference10639: Micro- and Nanotechnology Sensors, Systems, and Applications X
Session 8: Remote Sensing Techniques and Applications
Date and Time: Tuesday, April 17, 2018, 8:30 AM

This presentation summarizes the advances made in the last 50 years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on “Hyperspectral Remote Sensing of Vegetation” (Publisher:Taylor and Francis inc.) and well as some very recent research. The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming Italy’s ASI’s Prisma, Germany’s DLR’s EnMAP, Japanese HIUSI, NASA’s HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications’ knowledge base to large areas.


Disease detection and mitigation in a cotton crop with UAV remote sensing
Paper 10664-19

Author(s):  J. Alex, Texas A&M Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 4: Detecting Yield, Disease, and Water Stress from UAVs
Date and Time: Tuesday, April 17, 2018, 8:50 AM

A disease called cotton root rot (CRR) can devastate cotton crops unless a specific fungicide is applied at planting to protect the roots of the plants. Remote sensing has proven effective at identifying locations of the disease, and fungicide application can be limited to the areas where the fungus poses a problem. Since UAVs can efficiently provide highly detailed images of crops, image data of a field with CRR problems were collected with a UAV in 2015 and used to direct fungicide application in 2017. The result was that fungicide application was minimized without significantly reducing protection of the plants.


Applications of hyperspectral image analysis for precision agriculture
Paper 10639-42

Author(s):  Stan, Bayer CropScience LP (United States), et al.
Conference10639: Micro- and Nanotechnology Sensors, Systems, and Applications X
Session 8: Remote Sensing Techniques and Applications
Date and Time: Tuesday, April 17, 2018, 9:10 AM

With world projections of global population running to 9.5-10 billion by mid century, it has becoming apparent that increasing food production will soon become an existential problem. However, the world has been here before. In the mid 1950’s parts of the developing world, especially in Mexico and India were facing an existential food crisis. This situation helped spark a series of innovations that collectively became known as the “Green Revolution”. Recent advances in sensor technology, computer processing power, and algorithmic development have ushered in a new era of precision farming. It is now possible to obtain precise measurements of biological phenomena on very large scales. This development is part of a continuum of advances that have ushered in “Green Revolution 2.0”. Digital Farming technologies are the tip of the spear in this new era. In this paper we review the timeline and development of both biological and sensor technologies that are just now beginning to converge.


Experimental approach to detect water stress in ornamental plants using UAV-imagery
Paper 10664-20

Author(s):  Ana, Instituto de Agricultura Sostenible (Spain), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 4: Detecting Yield, Disease, and Water Stress from UAVs
Date and Time: Tuesday, April 17, 2018, 9:40 AM

Accurate, reliable and timely crop water status measurements could improve irrigation efficiency and optimize water use in agriculture. Containerized ornamental crops provide a unique opportunity to apply UAV platform due to relatively small area of production, a diversity of plant species, and unbuffered growing media requiring continual inputs of water; making UAV a timely alternative to on-ground data collection. This research evaluated the potential of UAV-based images to estimate crop water status of multiple taxa. An algorithm based on the object-based image analysis (OBIA) paradigm was developed to accurately identify water stressed and non-stressed plants.


Design-optimization and performances of multispectral (VIS-SWIR) photodetector and its array
Paper 10656-21

Author(s):  Jaydeep, Banpil Photonics, Inc. (United States), et al.
Conference10656: Image Sensing Technologies: Materials, Devices, Systems, and Applications V
Session 5: Advanced Photodetectors and Focal Plane Array (FPA)
Date and Time: Tuesday, April 17, 2018, 9:50 AM

A novel broadband (VIS-SWIR) photodetector is developed for focal plane array (FPA) for military, security, and industrial imaging applications. The photodetector is based on InGaAs and fabricated on InP substrate, exhibiting high sensitivity, high quantum efficiency, and yet cost-effective. In order to realize a small weight, power, and cost effectiveness (SWAP-C) camera, the photodetector must have low dark current at high operating temperatures, which saves power for cooling. This paper will explain photodetector structure, design-simulation for optimizing the parameters, and performance of the photodetector and its array. We investigate the device structure and the theory of the photodetector. Electrical and optical characteristics of the photodetectors will be also presented in this paper.


Evaluation of multispectral unmanned aerial systems for irrigation management
Paper 10664-23

Author(s):  José L. , Colorado State Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 5: Analytics for UAV-based Crop Management
Date and Time: Tuesday, April 17, 2018, 11:40 AM

Growing competition for water is incentivizing the implementation of deficit irrigation. Thus, there is a need to accurately map actual crop evapotranspiration (ETa) to more efficiently manage and document irrigation. An alternative is the use of remote sensing (RS) platforms. Unmanned Aerial Systems (UAS) can fly frequently and acquire very high spatial resolution images. Multispectral UASs (fixed-wing and multi-rotor) flew over irrigated corn fields, in northern Colorado, to evaluate the capabilities of the RS systems on irrigation management. Soil water content sensors were used in the evaluation. This study discusses the benefits of multispectral UAS platforms in irrigation management.


UAV videos to extend research to producers
Paper 10664-25

Author(s):  Louis, Geosystems Research Institute (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 6: Innovative UAV Applications
Date and Time: Tuesday, April 17, 2018, 1:50 PM

UAV’s are used to take short movies of ongoing corn research projects. Using Mississippi State University editing professionals the movies are narrated to describe the research in what we are doing and why. The view from the UAV above the plots offers a unique perspective on what is going in our fields. At a Field Day in 2016 the video was a spectacular success showing both the producers/scientists and the research plots. We will use our agricultual research, UAV and video editing expertise to create future educational short movies that will be useful for the agricultural boards, producers, Mississippi State University and perhaps even for use in the classroom to demonstrate clear visuals of our latest agricultural research projects.


Evaluating UAVs under a multi-platform system in modeling crop characteristics
Paper 10664-27

Author(s):  Gregory, Texas A&M Univ. (United States), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 6: Innovative UAV Applications
Date and Time: Tuesday, April 17, 2018, 2:30 PM


Evaluating the capabilities of Sentinel-2 and Tetracam RGB+3 for multi-temporal detection of thrips on capsicum
Paper 10664-28

Author(s):  Jayantrao D. , Tata Consultancy Services Ltd. (India), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 6: Innovative UAV Applications
Date and Time: Tuesday, April 17, 2018, 2:50 PM

Thrips is serious pest in Capsicum which goes undetected in initial phase so its timely detection is important. In this paper, we address the problem of detection of low infestation thrips on Capsicum using hyperspectral remote sensing data simulated to Sentinel-2 and Tetracam RGB+3 bands. The data is collected from 213 bands with wavelength ranging from 350 nm to 1052 nm over a period of 1 month using handheld spectroradiometer. We evaluated the performance of tuned random forest classifier by feeding different set of features such as full feature set of 213 bands, feature set selected by LASSO, feature set simulated to Sentinel-2 bands and Tetracam RGB+3. Classification accuracy of 92.81, 90.3, 85.13 and 87.45% was achieved when considering 213 bands, features selected by LASSO, Sentinel-2 like band simulations and Tetracam like simulations respectively.


Towards an AR Multiperspective Active Imaging Environment for Application Development
Paper 10644-78

Author(s):  Luke J. , Florida Polytechnic Univ. (United States), et al.
Conference10644: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Session PS1: Posters-Tuesday
Date and Time: Tuesday, April 17, 2018, 6:00 PM

AR in Multiperspective Environmental Imaging is a process using optical sensors such as LiDAR, Thermal Imaging, and 3D cameras to generate an Augmented Reality Environment inside of a graphics engine called Unity. These sensors controlled with multiple microcomputers that operate within a mesh-network system. The nodes communicate simultaneously with a central system to gather data from the different perspectives and sensors types to be processed in the Unity Engine. Using the Unity Engine we can display the collected Data in augmented reality to give a new mindset of the collected data.


Using hyperspectral sensors for crop vegetation status monitoring in precision agriculture
Paper 10664-32

Author(s):  Marius Cristian, Transilvania Univ. of Brasov (Romania), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM

The world is changing. Day by day we are facing with more and more changes regarding the climate, technology, economy and society. All of these place their mark on agro ecosystems. Major economic and environmental impacts can be obtained by providing water and nutritional supplement just to those plants that need them, only when they need and in proper quantities. In order to do this, a real time management of agricultural crops is necessary. The paper presents a solution for crop vegetation status monitoring in precision agriculture, based on hyperspectral sensors, namely on spectrometers, placed on an UAV (Unmanned Aerial Vehicle).


MoniSCAN: Software for multispectral monitoring of the crops vegetation status
Paper 10664-33

Author(s):  Marius Cristian, Transilvania Univ. of Brasov (Romania), et al.
Conference10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM

An efficient crops management, in the continuous changes context especially regarding climate, requires real time monitoring of soil resources and vegetation dynamics. A part of this process is the precision agriculture that supposes to investigate crops so that to allocate inputs as water and fertilizer for example, only to the plants that need them, at the proper moment and in proper quantities. For monitoring the crops vegetation status different solutions are available on the market. Most of them acquire spectral data, process and represent them on maps offering support for proper farmer decision. MoniSCAN is such a software developed in a research project.


Isolation of Highly Selective Phage-Displayed Oligopeptide Probes for Detection of Listeria monocytogenes in Ready-to-eat Food
Paper 10665-22

Author(s):  I-Hsuan, Auburn Univ. (United States), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM

Listeria monocytogenes is the major etiologic agent for foodborne Listeriosis in humans from consumption of ready-to-eat (RTE) food. According to FDA’s Bacterial Analysis Manual, L. monocytogenes in RTE food is detected via microbiological culture-based tests, qPCR, and pulsed-field gel electrophoresis. Those methods are time consuming and require dedicated laboratory facility. Thus, to develop a real-time L. monocytogenes biosensor, we isolated L. monocytogenes specific oligopeptides displayed on bacteriophages using modified biopanning procedures. In order to account for major temperature dependent morphological alterations of L. monocytogenes at 4°C versus 37°C, we used bacterial cells adapted to either temperature as the ligand in our biopanning. Those isolated probes can be used on our magnetoelastic biosensor platforms for real-time detection of L. monocytogenes in RTE foods stored at 4C or in human samples/fluids for bacterium adapted to 37°C.


Applications of convolutional neural networks (CNN) for food quality and safety using hyperspectral imaging
Paper 10665-25

Author(s):  Hoonsoo, Agricultural Research Service (United States), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM


Non-destructive method to detect artificially ripened banana using hyperspectral sensing and RGB imaging
Paper 10665-28

Author(s):  Mithun, Tata Consultancy Services Ltd. (India), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM

Fruits provide essential nutrition in most natural form suitable for human beings. They are best when ripened naturally. However, industrialization has provided many ways for quick ripening and for extended shelf life of fruits. Detection of artificial ripening could be done by sophisticated methods like chemical analysis in lab or visual inspection by experts, which may not be feasible all time. Of all the fruits banana is most consumed fruit around the world. Adulteration of banana can have devastating effects on masses on scale. It is figured, bananas are potentially ripened using carcinogens like Calcium Carbide(CaC2). In this paper, we propose and devise a novel and automatic method to classify the naturally and artificially ripened banana using spectral and RGB data. Our results show that using a Deep Learning (Neural Network) on RGB data, we achieve accuracy of up-to 90%.and using Random Forest and Multilayer Perceptron (MLP) feed forward Neural Network as classifiers on spectral data we can achieve accuracies of up-to 98.74% and 89.49% respectively.


Estimating paddy yields in North Korea using COMS geostationary satellite and GRAMI-rice model
Paper 10665-31

Author(s):  Jong-Min, Korea Aerospace Research Institute (Korea, Republic of), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session
Date and Time: Tuesday, April 17, 2018, 6:00 PM

For monitoring wide area of paddy, satellite is useful to estimate productivity using spectral bands. While a crop model is also useful for continuous simulation of crop growth information. Combining these methods may allow us to effectively and continuously monitor paddy rice information over continent areas. This study is for simulating productivity of paddy rice over North Korea area which is inaccessible regions based on a grid crop model and satellite images. Paddy productivities for North Korea were monitored based on synthetic uses of BRDF-adjusted NDVI and insolation from GOCI and MI sensors incorporated into the GRAMI-rice model. The crop model that uses remote sensing data as input was previously formulated to simulate crop productivities utilizing remote sensing imageries. We will report simulated outcomes in productivity in comparison with that from the national statistical data to verify the model performance in simulation for large scale areas


Study of visible imaging and near-infrared imaging spectroscopy for plant root phenotyping
Paper 10665-1

Author(s):  Thomas, CTR Carinthian Tech Research AG (Austria), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session 1: Hyperspectral and Multispectral Imaging for Foods
Date and Time: Wednesday, April 18, 2018, 8:00 AM

In modern agriculture drought is a major cause of low yields worldwide. Therefore, imaging systems that enable to study the interactions between plant and soil are a way towards better understanding of crop water supply. In this paper the combination of visible (VIS) imaging and near-infrared (NIR) imaging spectroscopy for plant root phenotyping is presented. The system provides increased image contrast which allows for a more reliable segmentation of the roots from the soil and additional information to be extracted. Moreover, it is possible to visualize the water distribution in the soil in close proximity to the roots.


Continuous gradient temperature Raman spectroscopy of unsaturated fatty acids: applications for fish lipids and rendered meat source identification
Paper 10665-3

Author(s):  C. Leigh, Agricultural Research Service (United States), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session 1: Hyperspectral and Multispectral Imaging for Foods
Date and Time: Wednesday, April 18, 2018, 8:40 AM

Continuous gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry to Raman spectroscopy. 20 Mb three-dimensional data arrays with 0.2°C increments and first/second derivatives allow complete vibrational assignments. We applied GTRS to eight unsaturated fatty acids with one double bond to six, and two phosphatidyl cholines, resulting in new 3D structures and insight into why diets high in fish are healthy. The highly improved lipid spectroscopy was also applied to differentiate pork and chicken meat and bone meal supplied from commercial rendering. Twenty percent pork mixed into chicken meal can be identified rapidly.


MCT-based shortwave infrared hyperspectral imaging system for the detection and quantification of adulterants in powder samples
Paper 10665-8

Author(s):  Hoonsoo, Agricultural Research Service (United States), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session 2: Sensing for Food Quality and Safety I
Date and Time: Wednesday, April 18, 2018, 11:40 AM


Non-targeted and targeted Raman imaging detection of chemical contaminants in food powders
Paper 10665-14

Author(s):  Jianwei, Agricultural Research Service (United States), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session 4: High Throughput Inspection
Date and Time: Wednesday, April 18, 2018, 2:50 PM

Economically motivated adulteration and fraud for food powders are emerging food safety risks that threaten the health of the general public. This study developed non-targeted and targeted methods to detect food powder adulterants based on Raman chemical imaging technique. Line-scan hyperspectral Raman images were acquired using a 785 nm line laser from selected powdered foods and ingredients mixed with representative adulterants and illegal additives. Raman data analysis algorithms were developed to fulfill non-targeted and targeted contaminant detection. For both methods, chemical images were created to map the contaminant particles mixed in the food powders.


Miniature near infrared spectroscopy spectrometer and information and communication technologies to guarantee the integrity of the EU high added-value acorn Iberian pig ham
Paper 10665-20

Author(s):  Ana, Univ. de Córdoba (Spain), et al.
Conference10665: Sensing for Agriculture and Food Quality and Safety X
Session 5: Visible and Near Infrared Imaging For Foods
Date and Time: Wednesday, April 18, 2018, 5:20 PM

This research is framed within FoodIntegrity, EU sponsored project(7th FP). The main goal of the research to be done is to provide industrials, producers and consumers with a methodology based in low-cost, portable and miniature NIRS sensors and information and communication technologies for process control and voluntary labelling, to guarantee the integrity of the EU high added-value as the “acorn Iberian pig ham”. The present study is focussed in transferring a database (470 samples) of IP tissue - analysed in a FOSS-NIRSystems 6500 (FNS6500) spectrometer, during the seasons 2009-2011 - to a portable/miniature instrument MicroNIR-Onsite, VIAVI (MN1700)


Necessary steps for the systematic calibration of a multispectral imaging system to achieve a targetless workflow in reflectance estimation: a study of Parrot SEQUOIA for precision agriculture
Paper 10644-42

Author(s):  Luis Mario, Parrot S.A. (France), et al.
Conference10644: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Session 9: Applications
Date and Time: Thursday, April 19, 2018, 8:20 AM

Comparison of remote sensing data from different weather conditions, time of day and geographic locations requires absolute reflectance. Reflectance estimation for precision agriculture demands detailed knowledge of imaging and ambient irradiance sensors. This work presents the difficulties and conditions for sensor characterization and modelling; and shows how these apply to automated calibration at an industrial scale using an integrating sphere and a reference spectrophotometer. Industry standard imaging of reflectance targets completes the system calibration. The consequences of the difference between calibration test bench geometry and the real world are discussed opening the road to a targetless workflow for multispectral cameras.


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